jeff-adapter-aml-gguf

The LoRA GGUF of jeff-adapter-aml (Jeff v1.3), for llama.cpp.

Anti-money-laundering review. Applies an institution's monitoring policy to a customer's last 30 days - clear, investigate, escalate or block.

This repository holds only the adapter. Load it on top of the base GGUF from mstrasser/jeff-base-gguf; there is no separate full model per adapter.

It works on either base format: Q8_0 (recommended, within 0.4 points of full precision on every adapter test) or Q4_K_M (within 0.4 points).

Files

aml.jeff.json has a temperature for each format (temperature_by_format: f16, q8_0, q4_k_m); use the one for your base format, so the probabilities stay calibrated.

Results

The same test, through llama.cpp: the base GGUF plus this LoRA GGUF, with the temperature refitted for each format. Running Jeff with llama.cpp

All results, and the same test at full precision against the untrained model and the base alone: jeffhub.ai/adapters/aml.

How to run it

Use llama.cpp commit cb7934c52ca8710994b2ecc19775ebefcfdb8d01 or newer: it needs the qwen35 architecture and LoRA on the output layer.

hf download mstrasser/jeff-base-gguf --local-dir jeff-gguf
hf download mstrasser/jeff-adapter-aml-gguf --local-dir jeff-gguf
cd jeff-gguf
llama-server -m models/base-q8_0.gguf -c 8192 -np 1 --lora-init-without-apply --lora loras/aml.gguf

Jeff is not a chat model. For each decision you run one forward pass over the prompt and read the probabilities of the answer codes; you never sample text.

1. Build the prompt exactly as Jeff does (the question, the state, the options as answer codes, and the changing state field under "Latest", then the chat template with thinking off). The Jeff repository builds it for you (jeff.model.decision_messages).
2. Tokenize without a beginning-of-sequence token, with special tokens parsed.
3. Run one forward pass over the whole prompt from an empty state, with this adapter active. Clear the state between prompts: Qwen3.5 has recurrent layers, so a reused state would carry over.
4. Read the probabilities. Take the logits of the first N answer-code tokens (token_ids[:N], N = the number of options), divide by the temperature for this adapter and your format, and apply a softmax over those N.

With llama-server, each decision is one POST /completion with "n_predict": 1, "cache_prompt": false, a lora list that names every loaded adapter (scale 1 for this one, 0 for the rest; an adapter left out of the list keeps scale 1.0), a logit bias of +1000 on every answer-code token of the request, and n_probs set to the number of options. From the llama.cpp library: load the base once with llama_model_load_from_file, this adapter once with llama_adapter_lora_init, and per request call llama_set_adapters_lora with scale 1, clear the memory, decode once and read llama_get_logits_ith(ctx, -1).

The full guide, with an example prompt and request: Running Jeff with llama.cpp.

Data and licence

Adapter licence: Apache-2.0.

Qwen3.5-0.8B notice: these weights were modified from Qwen3.5-0.8B by the Jeff project: jeff-base is a fine-tune of Qwen3.5-0.8B, and this adapter was trained on top of it. Qwen3.5-0.8B is Copyright 2026 Alibaba Cloud and licensed under the Apache License, Version 2.0; a copy of that licence is in LICENSE.

To confirm: whether the CDLA-Sharing-1.0 terms of the pattern rows affect the adapter's licence or only the data.

It was trained on:

• Synthetic institutions, customers and transactions. Licence: Released with the adapter under Apache-2.0 (made for this adapter) · Made by GLM 5.3 (own hardware), for some texts; everything else by code

  Simulated by code with fixed seeds; all names are invented. Some policy introductions, customer profiles and activity texts are written or reworded by GLM 5.3 and checked by code.

• IBM Transactions for Anti Money Laundering (AMLworld), laundering-pattern rows. Licence: Community Data License Agreement - Sharing - Version 1.0 (CDLA-Sharing-1.0); shared data, including modified data, must keep the same terms (open, but shared or changed data must keep the same terms) · Not made by a model

  Altman et al., "Realistic Synthetic Financial Transactions for Anti-Money Laundering Models", NeurIPS 2023 Datasets and Benchmarks. Fully synthetic; no real people or accounts.

Links

• The adapter at full precision: mstrasser/jeff-adapter-aml (revision v1.3)
• The base GGUF: mstrasser/jeff-base-gguf
• Adapter page: jeffhub.ai/adapters/aml
• Running Jeff with llama.cpp: jeffhub.ai/docs/llama-cpp

Jeff is an independent project. It uses the same request format as Jev but is not affiliated with or endorsed by TypeSafe, the makers of Jev.